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Logistic Regression and Prediction for Health Data
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Logistic Regression and Prediction for Health Data

Master statistical analysis of binary outcomes using logistic regression for healthcare data. Learn prediction and model assessment.

Course Cost

Free course

Intermediate

Skill Level

10 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science for Health Research Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

What you'll learn

  • Understand binary outcomes and calculate prevalence, risk ratios, and odds ratios

  • Use logistic regression for estimating associations between predictors and outcomes

  • Develop predictive models for healthcare applications

  • Assess model quality using calibration and discrimination metrics

  • Apply statistical analysis techniques to real healthcare data

Skills you'll gain

Logistic Regression
Statistical Analysis
Binary Outcomes
Probability Analysis
Data Science
Healthcare Analytics
Statistical Modeling
R Programming
Predictive Analytics
Model Assessment

This course includes:

4.6 Hours PreRecorded video

7 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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Certificate

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

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PROVIDED BY

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There are 3 modules in this course

This comprehensive course focuses on analyzing binary outcomes in healthcare data using logistic regression. Students learn fundamental statistical concepts, from basic two-group comparisons to advanced predictive modeling. The curriculum covers essential topics including prevalence calculation, risk ratios, odds ratios interpretation, and model assessment techniques. Through a combination of theoretical understanding and practical R programming exercises, learners develop skills in fitting logistic regression models, making predictions, and evaluating model performance using metrics like ROC curves and AUC.

Simple Comparisons of Binary Outcomes

Module 1 · 4 Hours to complete

Introducing Logistic Regression

Module 2 · 3 Hours to complete

Assessing the Predictive Accuracy of Logistic Regression Models

Module 3 · 3 Hours to complete

Fee Structure

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Logistic Regression and Prediction for Health Data

Intermediate

Skill Level

10 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.